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Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions


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Abad, P., Benito, S., & Lopez, C. (2013). A comprehensive review of value at risk methodologies. The Spanish Review of Financial Economics, 12(1), 15–32.AbadP.BenitoS.LopezC.2013A comprehensive review of value at risk methodologiesThe Spanish Review of Financial Economics121153210.1016/j.srfe.2013.06.001Search in Google Scholar

Allen, D., Singh, A., & Powell, R. (2011). Value at Risk estimation using extreme value theory. ECU Publications. Retrieved from http://ro.ecu.edu.au/ecuworks2011/.AllenD.SinghA.PowellR.2011Value at Risk estimation using extreme value theoryECU PublicationsRetrieved from http://ro.ecu.edu.au/ecuworks2011/.Search in Google Scholar

Alves, M., & Santos, P. (2013). Conditional EVT for VAR estimation: comparison with a new independence test. In J. Lita da Silva (Ed.), Advances in regression, survival analysis, extreme values, Markov processes and other statistical applications (pp. 183–191). Berlin, Germany: Springer.AlvesM.SantosP.2013Conditional EVT for VAR estimation: comparison with a new independence testInLita da SilvaJ.(Ed.),Advances in regression, survival analysis, extreme values, Markov processes and other statistical applications183191Berlin, GermanySpringer10.1007/978-3-642-34904-1_19Search in Google Scholar

Angelidis, T., Benos, A., & Degiannakis, S. (2007). A robust VAR model under different time periods and weighting schemes. Review of Quantitative Finance and Accounting, 28, 187–201.AngelidisT.BenosA.DegiannakisS.2007A robust VAR model under different time periods and weighting schemesReview of Quantitative Finance and Accounting2818720110.1007/s11156-006-0010-ySearch in Google Scholar

Artzner, P., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9, 203–228.ArtznerP.EberJ.-M.HeathD.1999Coherent measures of riskMathematical Finance920322810.1017/CBO9780511615337.007Search in Google Scholar

Balkema, A., & de Haan, L. (1974). Residual lifetime at great age. Annals of Probability, 2, 792–804.BalkemaA.de HaanL.1974Residual lifetime at great ageAnnals of Probability279280410.1214/aop/1176996548Search in Google Scholar

Bao, Y., Lee, T.-H., & Saltoglu, B. (2006). Evaluating predictive performance of Value-at-Risk models in emerging markets: a reality check. Journal of Forecasting, 25, 101–128.BaoY.LeeT.-H.SaltogluB.2006Evaluating predictive performance of Value-at-Risk models in emerging markets: a reality checkJournal of Forecasting2510112810.1002/for.977Search in Google Scholar

BCBS. (1996). Supervisory framework for the use of ‘backtesting’ in conjuction with the internal models approach to market risk capital requirements. Basel: Basel Committee on Banking Supervision. Retrieved from https://www.bis.org/publ/bcbs22.htm.BCBS1996Supervisory framework for the use of ‘backtesting’ in conjuction with the internal models approach to market risk capital requirementsBaselBasel Committee on Banking SupervisionRetrieved from https://www.bis.org/publ/bcbs22.htm.Search in Google Scholar

Bee, M., & Miorelli, F. (2010). Dynamic VaR models and the peaks over threshold method for market risk measurement: an empirical investigation during a financial crisis. Department of Economics Working Papers 1009, Department of Economics, University of Trento, Italia.BeeM.MiorelliF.2010Dynamic VaR models and the peaks over threshold method for market risk measurement: an empirical investigation during a financial crisisDepartment of Economics Working Papers 1009,Department of Economics, University of TrentoItaliaSearch in Google Scholar

Bhattacharyya, M., & Ritolia, G. (2008). Conditional VaR using EVT – towards a planned margin scheme. International Review of Financial Analysis, 17, 382–395.BhattacharyyaM.RitoliaG.2008Conditional VaR using EVT – towards a planned margin schemeInternational Review of Financial Analysis1738239510.1016/j.irfa.2006.08.004Search in Google Scholar

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.BollerslevT.1986Generalized autoregressive conditional heteroskedasticityJournal of Econometrics3130732710.1016/0304-4076(86)90063-1Search in Google Scholar

Bollerslev, T., Todorov, V., & Li, S. (2013). Jump tails, extreme dependencies, and the distribution of stock returns. Journal of Econometrics, 172(2), 307–324.BollerslevT.TodorovV.LiS.2013Jump tails, extreme dependencies, and the distribution of stock returnsJournal of Econometrics172230732410.1016/j.jeconom.2012.08.014Search in Google Scholar

Bommier, E. (2014). Peaks-over-threshold modelling of environmental data. Retrieved from https://uu.diva-portal.org/smash/get/diva2:760802/FULLTEXT01.pdf.BommierE.2014Peaks-over-threshold modelling of environmental dataRetrieved from https://uu.diva-portal.org/smash/get/diva2:760802/FULLTEXT01.pdf.Search in Google Scholar

Bystrom, H. (2001). Managing risks in tranquil and volatile markets using conditional extreme value theory. International Review of Financial Analysis, 13(2), 133–152.BystromH.2001Managing risks in tranquil and volatile markets using conditional extreme value theoryInternational Review of Financial Analysis13213315210.1016/j.irfa.2004.02.003Search in Google Scholar

Caires, S. (2009). A comparative simulation study of the annual Maxima and the peaks-over-threshold methods. “Hydraulic Engineering Reports” (Deltares Report 1200264-002). Retrieved from https://repository.tudelft.nl/islandora/object/uuid:143b0f1e-f61e-44ab-8da3-9241970d915b?collection=research.CairesS.2009A comparative simulation study of the annual Maxima and the peaks-over-threshold methods“Hydraulic Engineering Reports” (Deltares Report 1200264-002). Retrieved from https://repository.tudelft.nl/islandora/object/uuid:143b0f1e-f61e-44ab-8da3-9241970d915b?collection=research.Search in Google Scholar

Caporin, M. (2008). Evaluating Value-at-Risk measures in the presence of long memory conditional volatility. Journal of Risk, 10, 79–110.CaporinM.2008Evaluating Value-at-Risk measures in the presence of long memory conditional volatilityJournal of Risk107911010.21314/JOR.2008.172Search in Google Scholar

Chlebus, M. (2014). Market risk measuring using value at risk – two-step approach (PhD thesis), Faculty of Economic Sciences, University of Warsaw.ChlebusM.2014Market risk measuring using value at risk – two-step approach(PhD thesis),Faculty of Economic Sciences, University of WarsawSearch in Google Scholar

Christoffersen, P. (1998). Evaluating interval forecasting. International Economic Review, 39, 841–862.ChristoffersenP.1998Evaluating interval forecastingInternational Economic Review3984186210.2307/2527341Search in Google Scholar

Darbha, G. (2001). Value-at-Risk for fixed income portfolios – a comparison of alter-native models. Mumbai: National Stock Exchange. Retrieved from https://www.researchgate.net/publication/228607410_Value-at-Risk_for_Fixed_Income_portfolios-A_comparison_of_alternative_models.DarbhaG.2001Value-at-Risk for fixed income portfolios – a comparison of alter-native modelsMumbaiNational Stock ExchangeRetrieved from https://www.researchgate.net/publication/228607410_Value-at-Risk_for_Fixed_Income_portfolios-A_comparison_of_alternative_models.Search in Google Scholar

Da Silva, A., & de Melo Mendes, B. V. (2003). Value-at-Risk and extreme returns in Asian stock markets. International Journal of Business, 8(1), 24.Da SilvaA.de Melo MendesB. V.2003Value-at-Risk and extreme returns in Asian stock marketsInternational Journal of Business8124Search in Google Scholar

Embrechts, P., Kluppelberg, C., & Mikosch, T. (1997). Modelling extremal events for Insurance and Finance. Springer-Verlag, 295–305.EmbrechtsP.KluppelbergC.MikoschT.1997Modelling extremal events for Insurance and FinanceSpringer-Verlag29530510.1007/978-3-642-33483-2Search in Google Scholar

Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987–1007.EngleR.1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom InflationEconometrica50987100710.2307/1912773Search in Google Scholar

Engle, R., & Patton, A. (2001). What good is a volatility model? Quantitative Finance, Vol. 1, 237–245.EngleR.PattonA.2001What good is a volatility model?Quantitative Finance123724510.1016/B978-075066942-9.50004-2Search in Google Scholar

Engle, R. F., & Manganelli, S. (2004). CAViaR: conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22(4), 367–381.EngleR. F.ManganelliS.2004CAViaR: conditional autoregressive value at risk by regression quantilesJournal of Business & Economic Statistics22436738110.1198/073500104000000370Search in Google Scholar

Ergun, T., & Jun, J. (2010). Time-varying higher-order conditional moments and forecasting intraday VaR and expected shortfall. The Quarterly Review of Economics and Finance, 50, 264–272.ErgunT.JunJ.2010Time-varying higher-order conditional moments and forecasting intraday VaR and expected shortfallThe Quarterly Review of Economics and Finance5026427210.1016/j.qref.2010.03.003Search in Google Scholar

Fisher, R., & Tippett, L. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Proceedings of the Cambridge Philosophical Society, 180–190.FisherR.TippettL.1928Limiting forms of the frequency distribution of the largest or smallest member of a sampleProceedings of the Cambridge Philosophical Society18019010.1017/S0305004100015681Search in Google Scholar

Flugentiusson, H. (2012). Push it to the limit. Testing the usefulness of extreme value theory in electricity markets. Lund University Publications. Retrieved from http://lup.lub.lu.se/luur/download?-func=downloadFile&recordOId=3166413&file-OId=3166414.FlugentiussonH.2012Push it to the limit. Testing the usefulness of extreme value theory in electricity marketsLund University PublicationsRetrieved from http://lup.lub.lu.se/luur/download?-func=downloadFile&recordOId=3166413&file-OId=3166414.Search in Google Scholar

Gencay, R., & Selcuk, F. (2004). Extreme value theory and Value-at-Risk: Relative performance in emerging markets. International Journal of Forecasting, 20, 287–303.GencayR.SelcukF.2004Extreme value theory and Value-at-Risk: Relative performance in emerging marketsInternational Journal of Forecasting2028730310.1016/j.ijforecast.2003.09.005Search in Google Scholar

Gencay, R., Selcuk, F., & Ulugulyagci, A. (2003). High volatility, thick tails and extreme value theory in Value-at-Risk estimation. Insurance: Mathematics and Economics, 33, 337–356.GencayR.SelcukF.UlugulyagciA.2003High volatility, thick tails and extreme value theory in Value-at-Risk estimationInsurance: Mathematics and Economics3333735610.1016/j.insmatheco.2003.07.004Search in Google Scholar

Kourouma, L., Dupre, D., Sanfilippo, G., & Taramasco, O. (2010). Extreme value at risk and expected shortfall during financial crisis. Retrieved from https://ssrn.com/abstract=1744091;doi:10.2139/ssrn.1744091.KouroumaL.DupreD.SanfilippoG.TaramascoO.2010Extreme value at risk and expected shortfall during financial crisisRetrieved from https://ssrn.com/abstract=1744091;10.2139/ssrn.1744091Open DOISearch in Google Scholar

Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives, 3(2), 73–84.KupiecP.1995Techniques for verifying the accuracy of risk measurement modelsJournal of Derivatives32738410.3905/jod.1995.407942Search in Google Scholar

Lopez, J. (1999). Methods for evaluating Value-at-Risk estimates. Federal Reserve Bank of San Francisco Economic Review, 2, 3–17.LopezJ.1999Methods for evaluating Value-at-Risk estimatesFederal Reserve Bank of San Francisco Economic Review231710.2139/ssrn.1029673Search in Google Scholar

Manganelli, S., & Engle, R. (2001). Value at Risk Models in Finance. ECB Working Paper No. 75, available at SSRN: https://ssrn.com/abstract=356220ManganelliS.EngleR.2001Value at Risk Models in FinanceECB Working Paper No. 75, available at SSRN: https://ssrn.com/abstract=35622010.2139/ssrn.356220Search in Google Scholar

Marimoutou, V., Raggad, B., & Trabelsi, A. (2009). Extreme value theory and value at risk: application to oil market. Energy Economics, 31, 519–530.MarimoutouV.RaggadB.TrabelsiA.2009Extreme value theory and value at risk: application to oil marketEnergy Economics3151953010.1016/j.eneco.2009.02.005Search in Google Scholar

Marinelli C., d’Addona S., & Rachev T. (2007). A comparison of some univariate models for Value-at-Risk and expected shortfall. International Journal of Theoretical and Applied Finance, 10(06), 1043–1075.MarinelliC.d’AddonaS.RachevT.2007A comparison of some univariate models for Value-at-Risk and expected shortfallInternational Journal of Theoretical and Applied Finance10061043107510.1142/S0219024907004548Search in Google Scholar

Mutu, S., Balogh, P., & Moldovan, D. (2011). The efficiency of value at risk models on central and eastern European stock markets. International Journal of Mathematics and Computers in Simulation, 5, 110–117.MutuS.BaloghP.MoldovanD.2011The efficiency of value at risk models on central and eastern European stock marketsInternational Journal of Mathematics and Computers in Simulation5110117Search in Google Scholar

Nozari, M., Raei, S., Jahanguin, P., & Bahramgiri, M. (2010). A comparison of heavy-tailed estimates and filtered historical simulation: evidence from emerging markets. International Review of Business Papers, 6(4), 347–359.NozariM.RaeiS.JahanguinP.BahramgiriM.2010A comparison of heavy-tailed estimates and filtered historical simulation: evidence from emerging marketsInternational Review of Business Papers64347359Search in Google Scholar

Pagan, A. (1996). The econometrics of financial markets. Journal of Empirical Finance, 3, 15–102.PaganA.1996The econometrics of financial marketsJournal of Empirical Finance31510210.1016/0927-5398(95)00020-8Search in Google Scholar

Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 119–131.PickandsJ.1975Statistical inference using extreme order statisticsAnnals of Statistics311913110.1214/aos/1176343003Search in Google Scholar

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